Financial Services organisations today want to accelerate innovation by incorporating AI into their businesses. Machine Learning has become essential for use cases such as fraud detection, financial modeling, robo-advisors, client analytics, alternative data, etc. However, most companies struggle with preparing large datasets for analytics, managing the proliferation of ML frameworks, and moving models in development to production.
In this workshop, we’ll cover best practices for enterprises to use powerful open source technologies to simplify and scale your ML efforts. We’ll discuss how to leverage Apache Spark™, the de-facto data processing and analytics engine in enterprises today, for data preparation as it unifies data at massive scale across various sources. You’ll learn how to use ML frameworks (e.g. Tensorflow, XGBoost, Scikit-Learn, etc.) to train models based on different requirements. And finally, you can learn how to use Mlflow to track experiment runs between multiple users within a reproducible environment, and manage the deployment of models to production.
Join this half-day workshop to learn how unified analytics can bring data science and engineering together to accelerate your ML efforts.
What you will learn:
27 Baker Street, W1U 8EQ London